Class 16 – Wednesday, February 23

CVS Drugstore — Is not to be examined — CSV files yes

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Look both ways

• Back or ahead

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Agenda

• Dataset processing

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Downloads and redownloads

• Program csv_is_not_a_pharmacy.py

• Useful code for getting header information, as well as for getting actual data values

• Program lotta_books.py

• Examines a literal dataset based on the web dataset best_sellers.csv

• Program proper_and_sorted.py

• Prints out a properly capitalizes user-inputted names (the first letter of each name should be capitalized)

• Also prints out a list of properly capitalized names in sorted order

• Also prints out the first name that occurs alphabetically, and the alphabetically last one as well.

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To do

• Complete current homework

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Program csv_is_not_a_pharmacy.py

• Some program runs

Enter name of dataset: oceania.csv

dataset:

Country, Females, Males

Australia, 11175724, 11092660

Fiji, 421365, 439258

French Polynesia, 132082, 138682

New Caledonia, 125322, 125548

New Zealand, 2223281, 2144855

Papua New Guinea, 3359979, 3498287

Solomon Islands, 259909, 278239

Vanuatu, 117573, 122078

header:

['Country', 'Females', 'Males']

data:

['Country', 'Females', 'Males']

['Australia', 11175724, 11092660]

['Fiji', 421365, 439258]

['French Polynesia', 132082, 138682]

['New Caledonia', 125322, 125548]

['New Zealand', 2223281, 2144855]

['Papua New Guinea', 3359979, 3498287]

['Solomon Islands', 259909, 278239]

['Vanuatu', 117573, 122078]

Enter name of dataset: elevations.csv

dataset:

Location, Author, Max Height, Min Height

Narnia, Lewis, 4810, -10

Neverland, Milne, 426, -2

Oz, Baum, 1231, 679

Sleepy Hollow, Irving, 1629, 304

Stars Hollow, Sherman-Palladino, 725, 152

Toyland, MacDonough, 6187, 0

Wonderland, Carroll, 5895, -5

header:

['Location', 'Author', 'Max Height', 'Min Height']

data:

['Narnia', 'Lewis', 4810, -10]

['Neverland', 'Milne', 426, -2]

['Oz', 'Baum', 1231, 679]

['Sleepy Hollow', 'Irving', 1629, 304]

['Stars Hollow', 'Sherman-Palladino', 725, 152]

['Toyland', 'MacDonough', 6187, 0]

['Wonderland', 'Carroll', 5895, -5]

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Program lotta_books.py

• Examines a literal dataset based on the web dataset best_sellers.csv

• Program run

header: ['Name', 'Author', 'Language', 'Date', 'Sales']

sales column: 4

name column: 0

date column: 3

total sold: 1897000000

dates: [1865, 1939, 1754, 1605, 1997, 1937, 1943, 1954, 1859]

earliest: 1605

latest : 1997

average date: 1872

row with earliest book: 3

row with latest book : 4

info on earliest: ['Don Quixote', 'de Cervantes', 'Spanish', 1605, 500000000]

info on latest: ['Harry Potter', 'Rowling', 'English', 1997, 447000000]

name of earliest: Don Quixote

name of latest: Harry Potter

 

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🦆 © 2022 Jim Cohoon

Resources from previous semesters are available.